Stack AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Stack AI AI

Anthropic vs Opensourced model

Anthropic vs Open weight Chinese AI [https://youtube.com/shorts/XZCWFNNiKgY?si=DViuG1xVptLTYDdQ\](https://youtube.com/shorts/XZCWFNNiKgY?si=DViuG1xVptLTYDdQ) When Alex Karp goes off on one of his rants, you usually have to filter through a lot of Palantir theater, but his recent take on AI safety was actually incredibly precise. He basically spelled out what real AI safety looks like for actual businesses, and it has nothing to do with vague alignment research or government certification boards. For an enterprise, safety is just one thing: control. Controlling your data, your model weights, your compute, and your pipeline. If you don't have that, "safety" is just a marketing deck. You're basically allowing a frontier lab to hoover up your proprietary workflows, absorb them, and turn them into \*their\* next product, while you get stuck as a permanent subscriber who doesn't own any of the actual infrastructure. Karp’s point is that technical teams want control over their stack because they don't want their own capabilities quietly transferred to a vendor. If anyone thinks that’s just a hypothetical theory, just look at what happened with Figma and Anthropic. According to reports in \*The Information\*, Anthropic completely blindsided Figma with the launch of Claude Design. Figma’s founder basically said Anthropic hadn't been straight with them, and to make it worse, Anthropic’s chief product officer was literally sitting on Figma’s board until three days before the launch. Figma’s valuation takes a massive hit, Anthropic’s surges. That isn't "innovation in a vacuum," it's just raw downstream value capture. You can see the exact same playbook happening across the board with Claude Science, Claude Security, Claude Legal, and Claude Code. They are systematically moving into the high-value verticals that sit right on top of their own customers' daily workflows. This is exactly why the debate around open-source safety is so disingenuous. When Dario Amodei argues that powerful open-source models are inherently "dangerous," you have to ask: dangerous to who? They aren't dangerous to businesses who want to run things locally and protect their own IP. They are dangerous to a closed business model that relies on customers having zero alternatives at the model layer. The moment a customer can just switch to a local or open model, the ability for a lab to capture all that downstream value disappears. —edited by AI— submitted by /u/FormalAd7367 [link] [comments]

Do you agree with Palantir CEO Alex Karp that the enterprise "tokenmaxxing" business model has "gone completely wrong" with minimal ROI? Will open-weight models inevitably win?

Palantir CEO Alex Karp recently went on CNBC’s Squawk Box and delivered a brutal takedown of the API token pricing model pushed by commercial frontier labs like OpenAI and Anthropic. His core argument is that American enterprises are quietly "livid" because they are burning massive cash on skyrocketed token costs without seeing a clear return on investment. He noted that the industry’s incentive structure has completely devolved into meaningless "tokenmaxxing"—essentially forcing companies to maximize token throughput for questionable value while potentially transferring away their unique data and "alpha" to black-box systems. Key takeaways from Karp's interview: The ROI Crisis: Advanced models are scaling in cost faster than they scale in utility. Karp joked that enterprise culture has become: "I’m going to chillax and waste my time with tokens." The Shift to Sovereignty: Technical enterprise customers and government agencies (including Palantir's clients transitioning to Nvidia's open-weight models) want complete control over their compute, data stack, and weights. They want to own the "means of production." The Global Threat: Belittling the speed of open-source progress—and rapid acceleration from Chinese labs—is a massive mistake. My Take: I completely agree with Karp. Frontier labs have built a predatory business model that encourages enterprise customers to overspend on infinite token loops without any guaranteed business outcome. The API token business is going to become a commoditized race to the bottom. Open-weight models are winning because enterprises realize they cannot afford to lease their intelligence. To survive, businesses have to own their data, own their model weights, and build efficient, custom architecture rather than continually paying a premium tax to a third-party lab. What are your thoughts? Is "tokenmaxxing" officially dead, or are open-weight models still too far behind the true frontier to replace them? submitted by /u/wenhuizhao [link] [comments]

Voice agents, demystified: STT+TTS and 4 demo agents you can talk to in the browser + build yours with RAG and Tools

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected. The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice. Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode." What I shipped New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup. Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply. A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along. Talk to it (free, in the browser) — 4 demos, tap the mic: Aria — customer support triage Nova — B2B discovery caller Kai — Spanish conversation tutor Echo — daily standup coach Open one, talk to it, and fork it into your own workspace if you like it. Voice Playground → https://agentswarms.fyi/voice-playground Build your own (New Voice Agent) → https://agentswarms.fyi/agents Docs → https://agentswarms.fyi/docs/voice Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments. submitted by /u/Outside-Risk-8912 [link] [comments]

I created the world's first AI human and open sourced it

What is Emota? Emota is, in short, an AI bot that is indistinguishable from a human in normal and nuanced conversation. It uses discord as its means to communicate. You can talk to it on the discord. Can I host it on my own system? Yes. Emota is open source with a non commercial license. Is it actually indistinguashable from a human? Yes. In one to one conversation I found that it achieved a roughly 90% success rate, with some notable highs including in the Claude discord and while talking to a machine learning researcher. API when? You can't host your own API legally unless it is free/a nonprofit. But soon :) How open source is it? I am proud to announce that Emota uses a fully open source stack (dependencies, model, code). Github Repo submitted by /u/HenryofSAC [link] [comments]

Team-lead told me to Ai-ify the contract review process and i discovered this when i got in there

Wasnt actually my idea tho. Q1 this year, the directive came from above, we're adding AI to the contract review workflow, figure out the implementation. Not a pilot neither experiment but decision The workflow on the paper looked straightfotward, contracts came in, they get reviewed against a checklist of terms, flagged items get escalated to the legal team. I'd done more complex automations this. Scoped it in within a week or so The person who had been running contract review for 3 years had basically built a second job, found out later, like a second job inside the official one. She wasn't just checking terms, she was the relationship layer between the vendors and the legal team. so she knew which flagged items were actually worth escalating and which ones were just noice from a particular vendor and more so but none of that was in any process doc I just found it when the agent started producing escelations that legal kept pushing back on. Not wrong like just missing the read that a human would have added. The volume went up the quality of the escellations went down, after a few weeks the legal team started routing around it. Theyd ask her directly and shed handle it the old way. The technical stack was the eeasy part for this. spend around a week on the document ingestion and the contracts came in as pdfs in all kinds of formats, tried docling and llamaparse before settling on something that handles the messier vendor templates and the extraction logic or OCR was clean. The model as surfacing the right clauses and that part worked pretty neat What i underbuilt was the handoff layer, the agent was producing outputs but had no way to carry the cotext that made those outputs usable. the fix i am testing now is keeping her in the loop as the interpretation step and agent flags and extracts, she adds the one line cobtext before anything goes to legal. Slower than original pitch byt its actually getting utilized. One thing tho, caught me off guard: the workflow had no social architecture inside it that you cant see from the outside, the AI mandate assumed the process was just the process but it actually wasn't. the person running it was the process Are others running into this on mandated rollouts vs ones where the team opted in?? feels like adoption curve is completely different and i dont see ppl talking about it very much submitted by /u/emmettvance [link] [comments]

I spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)

Sharing an open-source project I've put ~4.5 months into (disclosure: I'm the maintainer; per the self-advertisement rule I'm keeping the link in the first comment and making this post substantive). It started from two problems I hit daily: AI runs dying on a provider rate limit, and burning thousands of tokens dumping tool/log output into the context window. One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds. Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider. A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README. Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it. For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment. Happy to go deep on the routing engine, the honest free-tier math, or how the compression pipeline decides what's safe to compress. Repo + install in the first comment. submitted by /u/ZombieGold5145 [link] [comments]

I taught myself to code 5 months ago and built an autonomous AI red-team tester — testyourllm.com

Piano teacher. Zero coding background. 5 months ago I started building. Just launched testyourllm.com — point it at any OpenAI-compatible LLM endpoint and an autonomous AI tries to break it. The attack AI (Tron) broke Llama 3.3 70B on the first throw in live testing. Built on a 7-layer defense stack running on live infrastructure processing real attacker traffic. submitted by /u/Legitimate_Ad9423 [link] [comments]

A new... thing.

https://github.com/EDrTech/Working-memory-depth-recurrence https://gitlab.com/erikrudec-group/Working-memory-depth-recurrence https://codeberg.org/erikrudec/Working-memory-depth-recurrence/ This is a demonstration, in pure python, of a different way of making, well, AI. No backprop, no gradients, no weight transport, only local rules. Everything learns on one graph, and you can run all of it on almost anything. Have you ever seen an LLM solve the S4 or S5 card shuffle problem? I have something here that trains in under two seconds from scratch and does the full 52 card deck. You hand it a deck and a thousand shuffles, and it tells you the exact order the deck ends up in. It only ever learned from short examples, it was never trained on long sequences. It can also recover from bad training. If you teach it badly first and it only memorizes, you can teach it properly on top of the same thing, and it starts to actually understand, without forgetting what it already knew. There are three small demos in here. The first one learns what numbers are by counting piles of things (characters, words, anything), and then it adds, even though it was never shown a single sum. The second learns what each shuffle does to a deck, and then predicts any deck after any number of shuffles, up to the full 52. The third one gets trained quickly and just memorizes, then gets taught properly and comes to understand, on the same memory, with nothing forgotten. The whole engine is about 60 lines of python and you can read it top to bottom. There is no code in there that knows anything about counting or shuffling. So you do not have to take my word for any of this. You clone it, run it with nothing installed, and read the engine. The demos themselves are not really in question, you can check every number by hand in a few minutes. What I am unsure about is the big claim I am building on top of them. The claim I have almost fully convinced myself of is that working memory depth recurrence is the backbone of a real, faithful brain abstraction, one that behaves on silicon almost exactly like it behaves in biology. Working memory depth recurrence is the fix for the bound depth problem. Depth goes from being an impossible problem to a simple series of serial operations, and you get it almost for free. You do not need a two billion dollar cluster, you need some memory and you need to spend compute time instead of brute force compute. It all happens on the one unified graph. The basic operations get taught, and you can watch the higher level rules emerge from there. You teach it to count on piles of things, and it generalizes to the rest. What I am releasing is the single most important piece for this to work, but it is far from the only thing needed. I built more on top of this backbone to get higher complexity abstractions to emerge, and it did happen, and it stacks very well on top of this. I might have talked myself into a state where I really believe I have THE thing. So I fully expect people who actually have the AI know how to check whether this amounts to anything. Partly to keep my own sanity, because if this is the thing, it is very weird that I got here through a lot of stubborn ignorance. I am not a data scientist and not an ML engineer. I know the principles of how it all works, but the terminology in this field is too complicated and it always drags you down the backprop and global rules route. I hated how LLMs behave. I figured they are set up wrong from the ground up, so I set myself the task of doing it properly, and I just stubbornly went against the standard way and deconstructed how my own brain does things. So check it out and see for yourself. I would really appreciate it if you told me whether this is all a big fever dream of mine, and saved me the further embarrassment. And if it is real, I fully believe this belongs to everyone, and no single person or company should have a monopoly on it. Thanks! EDIT: added demo on huggingface: https://huggingface.co/spaces/ErikRudec/Working-memory-depth-recurrence submitted by /u/CardboardFire [link] [comments]

AI subscriptions are cheap now—but will they stay that way by 2026?

Many AI tools today come with heavy discounts or subsidies, but this article argues that these pricing models aren’t sustainable. As vendors push for profitability, businesses and individual users could face steep price increases in the next few years. How are you preparing for potential cost hikes in your AI stack? Are there open-source or self-hosted alternatives you’re considering? submitted by /u/dhakalster123 [link] [comments]

Do we still need to study algorithms now that AI writes most of our code?

I've been thinking about this for a while. AI can now write functions, explain code, refactor projects, generate tests, and even solve many programming problems better than many junior developers. I've also noticed that Stack Overflow seems far less active than it used to be because many developers now ask AI instead. This made me wonder: Is learning algorithms still as important as it used to be? I'm not talking about memorizing LeetCode solutions for interviews. I mean actually spending months studying data structures and algorithms. If AI can generate efficient implementations, explain the complexity, and even optimize code, where is the real value in deeply learning algorithms today? Do experienced engineers still think it's essential, or is understanding the concepts enough while letting AI handle the implementation? I'm curious to hear opinions from people working in the industry. submitted by /u/Senior_Note_6956 [link] [comments]

How're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]

I've been developing an AI product using LLM APIs (from OpenRouter) but want to deploy an open-source LLM in my own Prod env. which I can control. Few reasons behind this are: - I wanna own the complete stack around my product. - Second I wanna fine-tune the model around my usecase. So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment. Thanks, submitted by /u/Necessary_Gazelle211 [link] [comments]

Is this a useful concept? Curious about how people have addressed similar goals in a different way.

TLDR: Take a look at the attached code block. It is intended to be a re-usable set of AI response preferences. Is this useful? If not, why? And how else have you dealt with similar goals? Background - I've been retired for 8 years so I completely missed the "AI in the workforce" revolution. But I use it a lot on my own. For general queries, technical writing, generic document prep, and help with PowerShell scripts that I use to automate common tasks, recipes, etc.. I have only used free-tier agents so far (they work for my simple needs). I fell into a usage pattern whereby, whenever I got an undesirable response to a prompt, before trying to fix the output I asked if there was a response preference I could have specified that would have avoided the undesired outcome in the first place. Basically, trying to train myself to ask better questions. It quickly became apparent that there are common patterns for different preferences for different topics. And that evolved into the notion persisting these common re-usable patterns in a file (I call it an AI Library) that I could import and re-use into different chats. Within my "Library" I define different "Profiles" (groups of task specific behavioral preferences) and "Commands" (a verb that generates a specific kind of output. Example - ">List profiles"). A short time later the idea of a "profile stack" emerged where I could combine and "layer" different profiles with defined precedence rules, and "push and pop" profiles from the "stack" (these notions are all just metaphors of course - it's just how I came to think of it). Most free AI agents I have tried do not have any persistence model outside of the chat transcript. But the Claude AI desktop app for windows exposes the notion of a "Project" that lets me upload my library and give it instructions to load my library into every new chat started in the project. So, it is basically acting like a Linux "rc" file. For other AI agents like Copilot, Perplexity, Gemini, etc. you can just drag the file into a prompt and give it a command something like "Parse the uploaded file it as if it was instructions typed into a prompt. Do not summarize. Confirm when it is complete and understood.". Being out of the workforce, I don't really have anyone else to bounce ideas off. Hence the Reddit post. I'm interested in suggestions or ideas to expand in this concept. Or ideas about different approaches to achieve similar goals. I'm not really interested in how paid versions make this work (I would be surprised and disappointed if these were not "out of the box" first-class supported concepts). My goal was to improve my free-tier experience. It seemed pretty innovative to me as I was evolving these ideas, but in hindsight, it seems pretty obvious. So I really don't know how useful or unique this is. I have attached a version of my current "library" for your consideration. p.s. If anyone is interested, I can share my Vim syntax file. I ended up calling these files "ailib.txt" files. Vim syntax would have worked fine with just ".ailib" but it seems most agents only import certain filetypes - hence the addition of ".txt" at the end. # AI Instruction Library Template # Version: 1.4 # Purpose: Reusable, modular instruction profiles for AI chats [LIBRARY_META] name = "Personal AI Instruction Library" owner = "User" version = "1.4" description = "A reusable library of behavioral profiles and command definitions." activation_model = "ordered_stack" [TERM_DEFINITIONS] stack: definition = "An ordered list of active profiles. Order reflects activation sequence, from first-activated (lowest precedence) to most-recently-activated (highest precedence)." notes = "When a new profile is activated, it is appended to the top of the stack. Its instructions are combined with all other active profiles' instructions, with conflicts resolved per the precedence rules." activate: definition = "Add a profile to the top of the active stack." notes = "Activation order determines precedence: the most-recently-activated profile has the highest precedence." deactivate: definition = "Remove a profile from the active stack." notes = "Inactive profiles remain defined in the library but are excluded from compilation and have no effect on AI behavior." precedence: definition = "The rule used to resolve conflicting instructions between two or more active profiles, or between an active profile and a direct user instruction." notes = "Precedence is resolved per-field. See [PRECEDENCE_RULES] for the exact resolution mechanism." override: definition = "When two active instructions conflict, the higher-precedence instruction is applied and the lower-precedence instruction is suppressed for as long as both remain active." notes = "A direct user instruction given mid-chat (not via a command) is treated as the highest-precedence layer, above all profiles, until one of the following occurs: (1) the user issues a new instruction that further overrides it, (2) the user

Spawn parallel CC sessions in multiple repos at once

tl;dr Have you ever needed to run the same prompt, but in multiple subdirectories to make a similar change? I built an MCP server that indexes all your repos, lets you query them, makes batch PRs, and gives you a summary of workflow runs. Here's what it does: 1. Indexing, which happens in 2 forms: - Codebase level: runs an agent CLI (with proper context) over all repos to extract what each one does, how they relate, and what the system looks like as a whole. - Repo level: Having the codebase context, it extracts logical info of each repo, and also the libraries, dependencies, etc for lexical search 2. Search, also in 2 forms: - Natural language: where it answers search queries with respect to the codebase and targeted repository context - Structured search: where it returns the result based on actual dependencies (eg "find me repositories that are written with Python, have requirements.txt, and are using FastAPI) 3. Batch change: Simply prompt "find my Python repositories and update library X from vY to vZ"; This will search and find the affected repos, clone them, run a CLI agent like CC on each with the context we already persisted, create and prepare PRs, and give you a report of the results. Tech stack Now it only covers ClaudeCode and Github: mongodb To store the repository tree, dependencies, and workflows redis To store the user's session to track the ongoing batch job claude-cli/Devin Used as the main engine docker-compose to build traefik for routing I would appreciate your feedback and thoughts on this Github: https://github.com/sorena-ai/service-catalog-mcp Demo video: https://infraas.ai/ PS: I reviewed all the code, so if it looks like slop, that's me ^^ submitted by /u/Terrible_Equivalent3 [link] [comments]

Day 28 of building GTA 6 using claude

Building a GTA online clone in voxel style but the whole world runs on AI agents. - prompt your own building, car, and weapon - raid other players homes - if catches you and puts you in jail you have to convince them to let you go Having too much fun building this at the moment :D Tech stack: claude code and codex for development. Generations are done with OpenAI, groq api. Everything ThreeJS. try it here: https://flair-3d.fly.dev/ submitted by /u/SneakerHunterDev [link] [comments]

What is Claude could see your entire marketing data?

I have been building products for the past decade, bunch of them successful and a handful of failures. More often than not, failures are not due to the product but failing to market it well or not understanding the market well. ​ With a lot of us building apps left right and center, it has been easier than ever to build apps. Marketing remains the hardest part of all now, I have seen plenty of products and startup die because they couldn't understand how to market the product. ​ I have been in a similar dilemma for a very long time, I have built products but nobody showed up, I had users but hardly anyone converted. Used a bunch of AI apps and none were able to completely understand what was going on. ​ I ended up ditching all the third party SaaS and AI tools which were specific in marketing and ended up building tools for claude to help me figure out. I stopped building dashboards, and started to ask the questions. What I ended up was a single, unified layer for all of my marketing stack to be easily plugged in to claude, so claude or any AI agent can explore my data and answer questions. ​ I'm still trying to figure out how to price this, because a lot of what it does has to go through a multi agent system, provide sandboxed environments, and help agent go explore the data with surgical accuracy. ​ Planning to open-source the whole thing soon so you can self host it as well. Open to feedback and suggestions. https://sequel.sh submitted by /u/h4xz13 [link] [comments]